R\'esum\'e-Driven Development: A Definition and Empirical Characterization

• URL: http://arxiv.org/abs/2101.12703v1
• Date: Fri, 29 Jan 2021 17:41:37 GMT
• Title: R\'esum\'e-Driven Development: A Definition and Empirical Characterization
• Authors: Jonas Fritzsch, Marvin Wyrich, Justus Bogner, Stefan Wagner
• Abstract summary: R'esum'e-Driven Development describes the overemphasis of trending technologies in both job offerings and resumes. We empirically investigated this phenomenon by surveying 591 software professionals in both hiring (130) and technical (558) roles.
• Score: 14.241792326365088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Technologies play an important role in the hiring process for software professionals. Within this process, several studies revealed misconceptions and bad practices which lead to suboptimal recruitment experiences. In the same context, grey literature anecdotally coined the term R\'esum\'e-Driven Development (RDD), a phenomenon describing the overemphasis of trending technologies in both job offerings and resumes as an interaction between employers and applicants. While RDD has been sporadically mentioned in books and online discussions, there are so far no scientific studies on the topic, despite its potential negative consequences. We therefore empirically investigated this phenomenon by surveying 591 software professionals in both hiring (130) and technical (558) roles and identified RDD facets in substantial parts of our sample: 60% of our hiring professionals agreed that trends influence their job offerings, while 82% of our software professionals believed that using trending technologies in their daily work makes them more attractive for prospective employers. Grounded in the survey results, we conceptualize a theory to frame and explain R\'esum\'e-Driven Development. Finally, we discuss influencing factors and consequences and propose a definition of the term. Our contribution provides a foundation for future research and raises awareness for a potentially systemic trend that may broadly affect the software industry.

Related papers

• Deploying Large Language Models With Retrieval Augmented Generation [0.21485350418225244]
  Retrieval Augmented Generation has emerged as a key approach for integrating knowledge from data sources outside of the large language model's training set. We present insights from the development and field-testing of a pilot project that integrates LLMs with RAG for information retrieval.
  arXiv  Detail & Related papers  (2024-11-07T22:11:51Z)
• Identifying Factors Contributing to Bad Days for Software Developers: A Mixed Methods Study [1.1545092788508224]
  The presence of friction can significantly hinder productivity, increase frustration, and contribute to low morale among developers. This research employed a mixed-method approach, including interviews, surveys, diary studies, and analysis of developer telemetry data. Findings revealed factors that cause "bad days" for developers and significantly impact their work and well-being.
  arXiv  Detail & Related papers  (2024-10-24T02:43:33Z)
• Fairness and Bias Mitigation in Computer Vision: A Survey [61.01658257223365]
  Computer vision systems are increasingly being deployed in high-stakes real-world applications. There is a dire need to ensure that they do not propagate or amplify any discriminatory tendencies in historical or human-curated data. This paper presents a comprehensive survey on fairness that summarizes and sheds light on ongoing trends and successes in the context of computer vision.
  arXiv  Detail & Related papers  (2024-08-05T13:44:22Z)
• Fairness in AI-Driven Recruitment: Challenges, Metrics, Methods, and Future Directions [0.0]
  Big data and machine learning has led to a rapid transformation in the traditional recruitment process. Given the prevalence of AI-based recruitment, there is growing concern that human biases may carry over to decisions made by these systems. This paper provides a comprehensive overview of this emerging field by discussing the types of biases encountered in AI-driven recruitment.
  arXiv  Detail & Related papers  (2024-05-30T05:25:14Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• Resist the Hype! Practical Recommendations to Cope With R\'esum\'e-Driven Development [14.241792326365088]
  We find empirical support for a tendency to overemphasize technology trends in r'esum'es and the application process. Among the software professionals, 82% believed that using trending technologies in their daily work would make them more attractive for potential future employers.
  arXiv  Detail & Related papers  (2023-07-06T08:32:38Z)
• The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practices [2.28438857884398]
  This study explores how, and to what extent, comparable research ethics requirements and norms have developed for AI research and data enrichment. Leading AI venues have begun to establish protocols for human data collection, but these are are inconsistently followed by authors.
  arXiv  Detail & Related papers  (2023-06-01T16:12:55Z)
• LLM-based Interaction for Content Generation: A Case Study on the Perception of Employees in an IT department [85.1523466539595]
  This paper presents a questionnaire survey to identify the intention to use generative tools by employees of an IT company. Our results indicate a rather average acceptability of generative tools, although the more useful the tool is perceived to be, the higher the intention seems to be. Our analyses suggest that the frequency of use of generative tools is likely to be a key factor in understanding how employees perceive these tools in the context of their work.
  arXiv  Detail & Related papers  (2023-04-18T15:35:43Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• Is 40 the new 60? How popular media portrays the employability of older software developers [78.42660996736939]
  We analyzed popular online articles and related discussions on Hacker News through the lens of employability issues and potential mitigation strategies. We highlight the importance of keeping up-to-date, specializing in certain tasks or technologies, and present role transitions as a way forward for veteran developers.
  arXiv  Detail & Related papers  (2020-04-13T10:00:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.